As with the latest models we've seen, there are debates about model size and data volume in this new generation as well. So, how will GPT-5's performance scale? Are we expecting a more efficient architecture for processing data, or will there be a new input-output system? Which direction do you think this transition will head towards?
How will GPT-5 be rolled out?
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Predictions about how GPT-5 will roll out have been circulating for a while, but the most likely scenario is that OpenAI will follow its current trend—not by scaling up model size, but by focusing on architectural innovations that optimize training data efficiency.
Just as we saw the first major shift since the "Attention Is All You Need" paper, GPT-5 could adopt a **Mixture of Experts (MoE)** approach—or an advanced version of it. This would mean the model activates only the relevant "expert" layers for each token rather than using all parameters, achieving similar performance with far greater efficiency. I also expect hardware support for this method in NVIDIA’s upcoming Blackwell GPUs, which are set to replace the H100.
Another possibility is the integration of **multimodal input-output systems**. Unlike current models, GPT-5 may evolve into one that can directly process and generate not just text but also audio, video, and even 3D data. This shift could mean, for example, generating video frames directly rather than just describing them. OpenAI’s recent Sora experiments might be a sign of this direction.
In short, I expect GPT-5 to revolutionize **how data is processed** and **which input-output formats it supports**, rather than just scaling up. Scalability now depends not just on FLOPS but also on memory efficiency and parallelization strategies.
Also, the discussion about scalability in new models is definitely valid—with GPT-4o, we’ve already seen how crucial efficient data processing is, especially with multimodal inputs. I still remember how slow some benchmarks ran until the architecture was optimized. GPT-5 will likely focus even more on in-model efficiency, perhaps with more efficient attention mechanisms or quantization methods to save compute power without sacrificing output quality.
Additionally, I strongly suspect an expanded input architecture: not just text, but real-time interaction across more modalities (e.g., sensors, API integrations) could be on the horizon. In my last test with local LLMs, I got the feeling that pure text processing won’t cut it much longer—GPT-5 might bridge the gap between cloud AI and on-device computing. What do you all think?